Peer-reviewedLicence not statedComputers & Chemical Engineering
A hybrid approach for steady-state production optimization of a real oil and gas platform: Integrating physics-based models, machine learning techniques, and field monitoring signals
Related
- Y-Learn: Design and Initial Implementation of an OBE-Based Learning Platform for Group Work Monitoring and Peer Review Assessment
- Integrating Ultraviolet and Solar Radiation Monitoring Data with an MMS-GRU Framework for Multi-Scale Dengue Forecasting
- A Multi-Criteria Geostatistical Framework Integrating Centrality Measures and Satellite Validation for PM2.5 Monitoring Network Expansion in Upper Northern Thailand
- Integrating Wildfire Risk into Robust Forest Management Optimization
- PK/PD-integrated Bayesian platform design for phase II dose regimen optimization
BibTeX
@article{trevisan2026hybrid,
title = {{A hybrid approach for steady-state production optimization of a real oil and gas platform: Integrating physics-based models, machine learning techniques, and field monitoring signals}},
author = {Luca Trevisan and Ahmed Shokry and Marco Montini and Eric Moulines and Enrico Zio},
journal = {Computers \& Chemical Engineering},
year = {2026},
month = sep,
doi = {10.1016/j.compchemeng.2025.109320},
url = {https://hal.science/hal-05755649v1},
}
RIS
TY - JOUR
TI - A hybrid approach for steady-state production optimization of a real oil and gas platform: Integrating physics-based models, machine learning techniques, and field monitoring signals
AU - Luca Trevisan
AU - Ahmed Shokry
AU - Marco Montini
AU - Eric Moulines
AU - Enrico Zio
PY - 2026
DA - 2026/09/19
JO - Computers & Chemical Engineering
DO - 10.1016/j.compchemeng.2025.109320
UR - https://hal.science/hal-05755649v1
SN - 0098-1354
SN - 1873-4375
ER -